Abstract
Background
Coronary artery disease (CAD) was the second leading cause of death for the past 3 years in Taiwan. The insulin‐like growth factor (IGF) system is considered a new risk factor of CAD because investigations show that the levels and bioactivity of IGF‐I and IGFBP‐3 (where IGFBP is insulin‐like growth factor‐binding protein) may be involved in elevating the risk of CAD. This study investigated the relationships among IGF‐I +1770, IGF‐I +6093, and IGFBP‐3 ‐202 genetic polymorphisms and CAD in the Taiwanese population.
Methods
A total of 581 subjects, including 390 non‐CAD controls and 191 patients with CAD, were recruited and the isolated DNA was subjected to real‐time polymerase chain to evaluate the effects of these three polymorphic variants on CAD.
Results
Our results showed a significant association between the IGF‐I +1770 gene polymorphism and increased risk of CAD. Furthermore, CAD patients with a minimum of one mutant C allele, T/C or C/C, in IGF‐I +1770 gene polymorphism had significantly high blood pressure including systolic blood pressure (SBP; P = 0.025) and diastolic blood pressure (DBP; P = 0.004), compared to CAD patients with T/T homozygotes. Moreover, CAD patients with a minimum of one mutant A allele, G/A or A/A, in the IGF‐I +6093 gene polymorphism had a 1.695‐fold elevated risk of congestive heart failure (CHF), compared to CAD patients with the G/G homozygote.
Conclusions
Polymorphism of IGF‐I +1770 was associated with increased CAD risk. In CAD patients, the contributions of IGF‐I +1770 and +6093 could be through the effect on blood pressure in CAD patients. J. Clin. Lab. Anal. 27:162–169, 2013. © 2013 Wiley Periodicals, Inc.
Keywords: IGF‐I, IGFBP‐3, single nucleotide polymorphism, coronary artery disease
INTRODUCTION
Insulin‐like growth factor‐I (IGF‐I), which is encoded on chromosome 12, is a single‐chain polypeptide consisting of 70 amino acids 1, 2. IGF‐I, a peptide hormone predominantly synthesized by the liver, is a significant mediator of growth hormone activities 3. IGF‐I is involved in cellular development, proliferation, differentiation, and cell survival, and thus, is related to tissue remodeling and homoeostasis 4, 5, 6. Moreover, IGF‐I has been reported to be involved in energy metabolism, such as glucose and lipid metabolism 1, 7. Because of the biological mechanisms of IGF‐I, it has been suggested to have a potential role in the pathogenesis of numerous diseases, such as tumorigenesis 8, 9, 10, rheumatic disease 11, osteoporosis 11, obstructive sleep apnea 11, and cardiovascular diseases (CVDs) 1, 12, 13. A widely investigated topic is the association between IGF‐I and cancer or CVD.
Previous studies have shown that elevated circulating IGF‐I levels may contribute to increased cancer risk. For example, high circulating IGF‐I is positively associated with an increased incidence of prostate cancer 14, 15, 16, premenopausal breast cancer 17, 18, 19, 20, and colon cancer 21. In the past decade, many researchers have attempted to explain the relationship between IGF‐I levels and CVD because patients suffering from hypopituitarism had an elevated prevalence of atheromatous plaques 22 and a higher proportion of cardiovascular mortality 27, 28. This finding implies that decreased circulating IGF‐I concentrations may be involved in increasing the risk for CVD. Previous research has shown that low circulating IGF‐I concentrations are associated with an increased risk of developing ischemic heart disease (IHD; 23, 24, 25, 26), Left ventricular (LV) dysfunction 27, heart failure 27, coronary artery disease (CAD), ischemic stroke 28, and myocardial infarctions (MI; 25, 29, 30).
In blood circulation, IGF‐I is circulated by a group of specific IGF‐binding proteins (IGFBPs), in which IGFBP‐3 is the primary IGF‐I carrier (>80%; 31). IGFBP‐3, which is encoded on chromosome 7p12, is primarily expressed in the liver. IGFBP‐3 is thought to regulate IGF‐I distribution and biological activity in IGF‐I by binding their specific cell surface receptors. Many researchers have recently investigated the relationship between IGFBP‐3 and CVD. For example, high IGFBP‐3 is thought to have a positive association with an elevated risk of developing IHD 32, coronary arteriosclerosis in men 33, and ischemic strokes 28.
Epidemiological studies of IGF‐I and IGFBP‐3 have shown that genetic or environmental factors may contribute to a person's susceptibility to CVD or cancer by affecting the protein concentrations of IGF‐I and IGFBP‐3 34, 35. Two single nucleotide polymorphism (SNP) located at positions +1770 (C > T, rs7965399) and +6093 (G > A, rs7978742) of IGF‐I 5′‐untranslated regions have influenced IGF‐I levels by increasing the expression of IGF‐ImRNA 36. In addition, an SNP located at position −202 (A > C, rs2854744) of IGFBP‐3 has been suggested to reduce the expression products of circulating IGFBP‐3 37, 38, 39.
Currently, IHD including CAD is ranked first among the top ten leading causes of death by the World Health Organization (WHO). In addition, heart disease is ranked second among the top 10 leading causes of death in the last 3 years in Taiwan. Well‐known risk factors associated with CAD include smoking, alcohol use, hypertension, hyperlipidemia, diabetes mellitus (DM), and obesity 40. The IGF system is a new risk factor in CAD because numerous studies have shown that the levels and bioactivity of IGF‐I and IGFBP‐3 may be involved in elevating the risk of CVD, such as CAD. However, no reports have focused on the association of the IGF‐I or IGFBP‐3 polymorphisms with the development of CAD. Therefore, we investigated the relationship between the three SNPs, IGF‐I +1770, IGF‐I +6093, IGFBP‐3 −202, and CAD in the Taiwanese population.
MATERIALS AND METHODS
Population
Between the years 2005 and 2009, a total of 581 Taiwanese patients (Han population) were recruited and analyzed in this study. The demographic and clinical characteristics of patients such as age, gender, family history, smoking habit, and blood pressure were documented. Of these, 390 patients (287 male and 103 female; mean age 65.71 ± 11.20 years) were diagnosed as CAD. Non‐CAD subjects consisted of 191 cases (130 male and 61 female; mean age 65.95 ± 12.44 years) randomly selected from the same geographic area. All subjects received an echocardiographic examination (Philips Healthcare, SONOS 7500) during their clinic visit. CAD group was diagnosed as ≥ Left ventricular 50% narrowing of the lumina of at least one major coronary artery by coronary angiography. Non‐CAD group were enrolled from the echocardiographic examination from the same hospitals. In addition, subjects with malignant diseases were excluded. All the patients were given informed consent, and were made aware of the study protocol. The study was approved by the hospital ethnic committee. Blood samples were collected via venipuncture, and were analyzed by the central research laboratory. The patients also received echocardiographic examination during the study period.
Genomic DNA Extraction
Venous blood from each subject was drawn into vacutainer tubes containing Ethylenediaminetetraacetic Acid (EDTA) and stored at 4°C. Genomic DNA was extracted by QIAamp DNA blood mini kits (Qiagen, Valencia, CA) according to the manufacturer's instructions. DNA was dissolved in Tris‐EDTA (TE) buffer [10 mM Tris (pH 7.8), 1 mM EDTA] and then quantitated by a measurement of OD260. Final preparation was stored at −20°C and used as templates for polymerase chain reaction (PCR).
Real‐Time PCR
Allelic discrimination of the IGF‐I +1770, IGF‐I +6093, and IGFBP‐3 −202 genetic polymorphisms was assessed with the ABI StepOne™ Real‐Time PCR System (Applied Biosystems, Foster City, CA) and analyzed using SDS vers. 3.0 software (Applied Biosystems) with the TaqMan assay 34. The final volume for each reaction was 5 μl, containing 2.5 μl TaqMan Genotyping Master Mix, 0.125 μl TaqMan probe mix, and 10 ng genomic DNA. The real‐time PCR included an initial denaturation step at 95°C for 10 min, followed by 40 cycles of 95°C for 15 sec and 60°C for 1 min.
Statistical Analysis
The average age are presented as the mean ± SE. A Mann–Whitney U test and a Fisher's exact test were used to compare the differences of age as well as demographic characteristics distributions between non‐CAD and patients with CAD, since the small sample size was present in some categorical variables. Hardy–Weinberg equilibrium was assessed using a goodness‐of‐fit χ2 test for biallelic markers. The odds ratios (ORs) with their 95% confidence intervals (CIs) of the association between genotype frequencies and CAD susceptibility as well as clinical characteristics were estimated by multiple logistic regression models. A P‐value < 0.05 was considered significant. The data were analyzed on SAS statistical software (Version 9.1, 2005; SAS Institute, Inc., Cary, NC).
RESULTS
Table 1 shows the demographic and clinical characteristics of our study groups, comprising non‐CAD and CAD patients. The CAD group and non‐CAD group have significant differences in atrial fibrillation (AF), aspirin use in the past 7 days, recent (<24 hr) severe angina, cardiac marker (cardiac troponin I) elevation (P < 0.001), and congestive heart failure (CHF; P = 0.001). Second, there are also statistical differences in hypertension (P = 0.011) and increased CAD risk (more than three; P = 0.014).
Table 1.
Demographics and Clinical Pathological Features of Subjects in non‐CAD and CAD Patients (N = 581)
| Non‐CAD | CAD | ||
|---|---|---|---|
| (n = 191) | (n = 390) | P‐value | |
| Gender | |||
| Malea | 130 (68.1%) | 287 (73.6%) | 0.164 |
| Age (years)b | 65.95 ± 12.44 | 65.71 ± 11.20 | 0.813 |
| Height (cm) | 161.59 ± 8.49 | 161.75 ± 8.48 | 0.824 |
| Weight (kg) | 66.21 ± 12.73 | 66.94 ± 12.59 | 0.514 |
| BMI (kg/m2) | 25.05 ± 3.75 | 25.56 ± 4.40 | 0.418 |
| AF positive (%) | 49 (25.7%) | 39 (10.0%) | <0.001 |
| CAD risk ≥ 3 (%) | 92 (48.2%) | 230 (59.0%) | 0.014 |
| Age > 65 year (%) | 109 (57.1%) | 210 (53.8%) | 0.463 |
| Family history (%) | 44 (23.0%) | 81 (20.8%) | 0.532 |
| Hypertension (%)c | 121 (63.4%) | 287 (73.6%) | 0.011 |
| Diabetes mellitus (%)d | 68 (35.6%) | 166 (42.6%) | 0.108 |
| Active smoker (%)e | 75 (39.3%) | 170 (43.6%) | 0.322 |
| Cholesterol>200 (%) | 72 (38.3%) | 158 (40.7%) | 0.578 |
| Aspirin use in the past 7 days (%) | 50 (26.3%) | 154 (41.4%) | <0.001 |
| Recent (<24 hr) severe angina (%) | 111 (58.1%) | 284 (73.2%) | <0.001 |
| Cardiac markers elevation (%)f | 76 (39.8%) | 223 (57.2%) | <0.001 |
| Stroke | 24 (13.4%) | 41 (11.0%) | 0.416 |
| CHF | 66 (35.9%) | 86 (22.8%) | 0.001 |
| SBP (mmHg) | 131.92 ± 19.79 | 132.32 ± 21.30 | 0.831 |
| DBP (mmHg) | 78.74 ± 14.75 | 79.09 ± 15.31 | 0.797 |
Data were presented as a number (percentage) with χ2 test/Fisher exact test.
Mean ± SD with independent two‐sample t‐test.
Hypertension was defined as systolic and/or diastolic blood pressure above 140/90 mmHg.
Diabetes mellitus was defined as a fasting plasma glucose level of 126 mg/dl or more, or a 2‐hr postload plasma glucose level of 200 mg/dl or more.
Active smoker was defined as a person who currently smoked at least one cigarette/day.
Cardiac marker: cardiac troponin I.
AF, atrial fibrillation; BMI, body mass index; CAD, coronary artery disease; CHF, congestive heart failure; DBP, diastolic blood pressure; SBP, systolic blood pressure.
Table 2 shows the OR and 95% CI of CAD patients associated with genotypic frequencies of IGF‐I and IGFBP‐3 in the Taiwanese population. In our recruited control group, the frequencies of IGF‐I +1770 T/C (P = 0.182, χ2 value: 1.780), IGF‐I +6093 G/A (P = 0.886, χ2 value: 0.021), and IGFBP‐3 −202 A/C (P = 0.232, χ2 value: 1.429) were in Hardy–Weinberg equilibrium, respectively. For the IGF‐I +1770 genetic polymorphism, C/C homozygote participants had a 1.776‐fold (95% CI = 1.044–3.020) significant elevated risk of CAD compared to T/T homozygote participants. Neither the IGF‐I +6093 genetic polymorphism nor the IGFBP‐3 −202 genetic polymorphism has a significant association with CAD risk.
Table 2.
Odds Ratio (OR) and 95% Confidence Interval (CI) of CAD Patients Associated with Genotypic Frequencies of IGF‐I and IGFBP‐3
| Variable | Non‐CAD (n = 191) (%) | CAD (n = 390) (%) | OR (95% CI) | P‐value |
|---|---|---|---|---|
| IGF‐I +1770 | ||||
| TT | 63 (33.0%) | 113 (29.0%) | 1.00 | |
| TC | 101 (52.9%) | 191 (49.0%) | 1.054 (0.713–1.559) | P = 0.791 |
| CC | 27 (14.1%) | 86 (22.1%) | 1.776 (1.044–3.020) | P = 0.033* |
| TT | 63 (33.0%) | 113 (29.0%) | 1.00 | |
| TC + CC | 128 (67.0%) | 277 (70.0%) | 1.207 (0.831–1.751) | P = 0.323 |
| T | 227 (59.4%) | 345 (53.5%) | 1.00 | |
| C | 155 (40.6%) | 439 (46.5%) | 1.275 (0.995–1.634) | P = 0.055 |
| IGF‐I +6093 | ||||
| GG | 138 (72.3%) | 274 (70.3%) | 1.00 | |
| GA | 49 (25.7%) | 109 (27.9%) | 1.120 (0.755–1.662) | P = 0.572 |
| AA | 4 (2.1%) | 7 (1.8%) | 0.881 (0.254–3.062) | P = 0.842 |
| GG | 138 (72.3%) | 274 (70.3%) | 1.00 | |
| GA + AA | 53 (27.7%) | 116 (29.7%) | 1.102 (0.751–1.618) | P = 0.619 |
| G | 325 (85.1%) | 657 (84.2%) | 1.00 | |
| A | 57 (14.9%) | 123 (15.8%) | 1.067 (0.759–1.501) | P = 0.708 |
| IGFBP‐3 −202 | ||||
| AA | 108 (56.5%) | 221 (56.7%) | 1.00 | |
| AC | 67 (35.1%) | 149 (38.2%) | 1.087 (0.752–1.572) | P = 0.658 |
| CC | 16 (8.4%) | 20 (5.1%) | 0.611 (0.304–1.226) | P = 0.162 |
| AA | 108 (56.5%) | 221 (56.7%) | 1.00 | |
| AC + CC | 83 (43.5%) | 169 (43.3%) | 0.995 (0.702–1.411) | P = 0.995 |
| A | 283 (74.1%) | 591 (75.8%) | 1.00 | |
| C | 99 (25.9%) | 189 (24.2%) | 0.914 (0.690–1.211) | P = 0.532 |
The odds ratio (OR) with their 95% confidence intervals were estimated by logistic regression.
*P value < 0.05
Table 3 shows the comparison of demographics and pathological features between two genotypes of IGF‐I and IGFBP‐3 for the CAD patients group. For the IGF‐I +1770 genetic polymorphism, participants with a minimum of one mutant C allele, T/C or C/C, had significantly high blood pressure, including systolic blood pressure (SBP) and diastolic blood pressure (DBP), compared to T/T homozygote participants. For the IGF‐I +6093 genetic polymorphism, participants with a minimum of one mutant A allele, G/A or A/A, had significantly high blood pressure, including SBP and DBP, compared to G/G homozygote participants. For the IGFBP‐3 −202 genetic polymorphism, no significant association between the IGFBP‐3 −202 genetic polymorphism and the demographics and pathological features of CAD patients was found.
Table 3.
Comparison of Demographics and Pathological Features Between Two Genotypes of IGF‐I and IGFBP‐3 for CAD Patients Group
| IGF‐I +1770 TT (n = 113) | IGF‐I +1770 TC + CC (n = 277) | P‐value | |
|---|---|---|---|
| Age (years) | 65.19 ± 11.53 | 65.92 ± 11.07 | 0.562 |
| Height (cm) | 162.55 ± 7.37 | 161.43 ± 8.89 | 0.237 |
| Weight (kg) | 68.06 ± 12.92 | 66.48 ± 12.44 | 0.260 |
| BMI (kg/m2) | 25.66 ± 4.04 | 25.51 ± 4.55 | 0.760 |
| SBP(mmHg) | 128.54 ± 19.20 | 133.90 ± 21.96 | 0.025* |
| DBP(mmHg) | 75.65 ± 13.61 | 80.52 ± 15.77 | 0.004* |
| IGF‐I +6093 | IGF‐I +6093 | ||
| GG (n = 274) | GA+AA (n = 116) | ||
| Age (years) | 65.60 ± 10.86 | 65.97 ± 12.00 | 0.762 |
| Height (cm) | 162.21 ± 8.73 | 160.68 ± 7.77 | 0.104 |
| Weight (kg) | 67.75 ± 12.94 | 65.01 ± 11.53 | 0.051 |
| BMI (kg/m2) | 25.73 ± 4.63 | 25.13 ± 3.82 | 0.213 |
| SBP(mmHg) | 130.70 ± 20.46 | 136.09 ± 22.80 | 0.023* |
| DBP(mmHg) | 77.70 ± 14.55 | 82.33 ± 16.58 | 0.006* |
| IGFBP‐3 | IGFBP‐3 | ||
| AA (n = 221) | AC+CC (n = 169) | ||
| Age (years) | 65.59 ± 11.21 | 65.86 ± 11.22 | 0.813 |
| Height (cm) | 161.80 ± 7.87 | 161.70 ± 9.23 | 0.913 |
| Weight (kg) | 66.81 ± 13.15 | 67.11 ± 11.84 | 0.817 |
| BMI (kg/m2) | 25.43 ± 4.17 | 25.71 ± 4.70 | 0.536 |
| SBP(mmHg) | 133.06 ± 21.32 | 131.34 ± 21.30 | 0.432 |
| DBP(mmHg) | 79.85 ± 15.55 | 78.09 ± 14.99 | 0.264 |
Data were presented as mean ± SD with independent two‐sample t‐test.
*Significance P‐value < 0.05.
Table 4 shows the comparison of clinical features between two genotypes of the IGF‐I +1770 genetic polymorphism for the CAD patient group. There was no significant association between the IGF‐I +1770 genetic polymorphism and clinical features in the CAD patient group. However, in IGF‐I +6093 genetic polymorphism, participants with a minimum of one mutant A allele, G/A or A/A, had a 1.695‐fold (95%, CI = 1.026–2.802) elevated risk of CHF, compared to G/G homozygote patients. In addition, patients with a minimum of one mutant A allele, G/A or A/A, were less likely to be male, compared to G/G homozygote patients (P = 0.019) (Table 5).
Table 4.
Comparison of Clinical Feature Between Two Genotypes of IGF‐I +1770 Gene Polymorphism for CAD Patients Group
| IGF‐I +1770 | IGF‐I +1770 | |||
|---|---|---|---|---|
| TT (n = 113) | TC + CC (n = 277) | OR (95% CI) | P‐value | |
| Male | 87 (77.0%) | 200 (72.2%) | 0.776 (0.466–1.294) | 0.330 |
| AF positive (%) | 7 (6.2%) | 32 (11.6%) | 1.978 (0.846–4.623) | 0.110 |
| CAD risk ≥ 3 (%) | 61 (54.0%) | 169 (61.0%) | 1.334 (0.858–2.075) | 0.201 |
| Age >65 year (%) | 58 (51.3%) | 152 (54.9%) | 1.153 (0.744–1.787) | 0.524 |
| Family history (%) | 20 (17.7%) | 61 (22.0%) | 1.313 (0.750–2.300) | 0.340 |
| Hypertension (%) | 77 (68.1%) | 210 (75.8%) | 1.465 (0.905–2.373) | 0.119 |
| Diabetes mellitus (%) | 43 (38.1%) | 123 (44.4%) | 1.300 (0.831–2.034) | 0.250 |
| Active smoker (%) | 51 (45.1%) | 119 (43.0%) | 0.916 (0.590–1.422) | 0.695 |
| Cholesterol > 200 (%) | 45 (39.8%) | 113 (41.1%) | 1.054 (0.674–1.648) | 0.817 |
| Aspirin use in the past 7 days (%) | 44 (41.9%) | 110 (41.2%) | 0.971 (0.614–1.535) | 0.901 |
| Recent (<24 hr) sever angina (%) | 83 (74.1%) | 201 (72.8%) | 0.936 (0.568–1.542) | 0.796 |
| Cardiac markers elevation (%) | 67 (59.3%) | 156 (56.3%) | 0.885 (0.568–1.380) | 0.590 |
| Stroke | 13 (12.4%) | 28 (10.5%) | 0.829 (0.412–1.670) | 0.600 |
| CHF | 17 (16.2%) | 69 (25.4%) | 1.759 (0.979–3.164) | 0.057 |
Table 5.
Comparison of Clinical Features Between Two Genotypes of IGF‐I +6093 Gene Polymorphism for CAD Patients Group
| IGF‐I +6093 | IGF‐I +6093 | |||
|---|---|---|---|---|
| GG (n = 274) | GA + AA (n = 116) | OR (95% CI) | P‐value | |
| Male | 211 (77.0%) | 76 (65.5%) | 0.567 (0.353–0.912) | 0.019 |
| AF positive (%) | 23 (8.4%) | 16 (13.8%) | 1.746 (0.886–3.443) | 0.104 |
| CAD risk ≥ 3 (%) | 163 (59.5%) | 67 (57.8%) | 0.931 (0.559–1.446) | 0.751 |
| Age >65 year (%) | 148 (54.0%) | 62 (53.4%) | 0.977 (0.632–1.511) | 0.918 |
| Family history (%) | 59 (21.5%) | 22 (19.0%) | 0.853 (0.494–1.473) | 0.568 |
| Hypertension (%) | 198 (72.3%) | 89 (76.7%) | 1.265 (0.763–2.097) | 0.361 |
| Diabetes mellitus (%) | 114 (41.6%) | 52 (44.8%) | 1.140 (0.736–1.767) | 0.556 |
| Active smoker (%) | 123 (44.9%) | 47 (40.5%) | 0.836 (0.538–1.299) | 0.426 |
| Cholesterol > 200 (%) | 110 (40.4%) | 48 (41.4%) | 1.040 (0.668–1.617) | 0.863 |
| Aspirin use in the past 7 days (%) | 109 (41.9%) | 45 (40.2%) | 0.930 (0.593–1.461) | 0.754 |
| Recent (<24 hr) sever angina (%) | 200 (73.5%) | 84 (72.4%) | 0.945 (0.580–1.540) | 0.820 |
| Cardiac markers elevation (%) | 159 (58.0%) | 64 (55.2%) | 0.890 (0.575–1.379) | 0.602 |
| Stroke | 27 (10.3%) | 14 (12.6%) | 1.251 (0.629–2.488) | 0.523 |
| CHF | 52 (19.8%) | 34 (29.6%) | 1.695 (1.026–2.802) | 0.038* |
1Data were presented as mean ± SD with independent two‐sample t‐test.
*Significance P‐value < 0.05.
Table 6 shows the comparison of clinical features between two genotypes of the IGFBP‐3 −202 genetic polymorphism for the CAD group. Our results found that patients with a minimum of one mutant C allele, A/C or C/C, had a 0.459‐fold (95%, CI = 0.222 to 0.947) significantly decreased risk of stroke, compared to A/A homozygote patients.
Table 6.
Comparison of Clinical Features Between Two Genotypes of IGFBP‐3 ‐202 Gene Polymorphism for CAD Patients Group
| IGFBP‐3 AA | IGFBP‐3 AC + CC | |||
|---|---|---|---|---|
| (n = 221) | (n = 169) | OR (95% CI) | P‐value | |
| Male | 161 (72.9%) | 126 (74.6%) | 1.092 (0.692–1.722) | 0.705 |
| AF positive (%) | 23 (10.4%) | 16 (9.5%) | 0.900 (0.460–1.263) | 0.759 |
| CAD risk ≥ 3 (%) | 133 (60.2%) | 97 (57.4%) | 0.891 (0.593–1.339) | 0.580 |
| Age >65 year (%) | 115 (52.0%) | 95 (56.2%) | 1.183 (0.791–1.770) | 0.412 |
| Family history (%) | 44 (19.9%) | 37 (21.9%) | 1.128 (0.689–1.844) | 0.632 |
| Hypertension (%) | 169 (76.5%) | 118 (69.8%) | 0.712 (0.453–1.119) | 0.140 |
| Diabetes mellitus (%) | 100 (45.2%) | 66 (39.1%) | 0.775 (0.516–1.165) | 0.220 |
| Active smoker (%) | 103 (46.6%) | 67 (39.6%) | 0.753 (0.501–1.129) | 0.169 |
| Cholesterol > 200 (%) | 83 (37.7%) | 75 (44.6%) | 1.331 (0.885–2.003) | 0.170 |
| ASA use in the past 7 days (%) | 88 (42.1%) | 66 (40.5%) | 0.936 (0.617–1.418) | 0.754 |
| Recent (<24 hr) sever angina (%) | 167 (75.9%) | 117 (69.6%) | 0.728 (0.464–1.143) | 0.167 |
| Cardiac markers elevation (%) | 133 (60.2%) | 90 (53.3%) | 0.754 (0.503–1.130) | 0.171 |
| Stroke | 30 (14.0%) | 11 (7.0%) | 0.459 (0.222–0.947) | 0.032* |
| CHF | 52 (24.0%) | 34 (21.3%) | 0.856 (0.524–1.398) | 0.535 |
1Data were presented as mean ± SD with independent two‐sample t‐test.
*Significance P‐value < 0.05.
DISCUSSION
Numerous current investigations indicate that decreased circulating IGF‐I levels may contribute to many cardiovascular risk factors, such as oxidized Low‐density lipoprotein (LDL), smoking, diabetes, obesity, and reduced coronary flow 41, 42, 43, 44. Thus, IGF‐I reduces the risk of CVD by reducing risk factors, such as atherogenesis 45, 46, 47, stabilizing plaque 47, antiplatelet activity 48, 49, and exerting endothelial survival 50, 51. Effectors that circulate concentrations of IGF‐I and IGFBP‐3 include both genetic 52, 53 and environmental factors, such as aging 54, diet 55, 56, sex 54, and other personal and lifestyle factors 19, 57.
Currently, GC repeating in the IGF‐I promoter region is a frequently investigated polymorphism that may have a suppressive effect on IGF‐I expression. Noncarriers of the 192 nt (wild type) allele were suggested to have an elevated risk of type 2 diabetes 58, MI, and heart failure 59 by affecting the circulating IGF‐I levels 60. We show higher polymorphic frequency of the IGF‐I +1770 C/C homozygote in CAD patients compared to the non‐CAD group with 22.1% and 14.1% (OR = 1.776, P = 0.033), respectively. The polymorphism of IGF‐I +1770, which was first described by Cheng et al., may be associated with prostate cancer risks in five racial or ethnic groups: African Americans, Native Hawaiians, Japanese, Latinos, and Caucasians 36, 61. Furthermore, Zahrani et al. showed that the C allele of IGF‐I +1770 expressed higher IGF‐I plasma levels, compared to the A allele in breast cancer patients 62. In Table 2, our data show that the CC homozygote of IGF‐I +1770 gene polymorphism may increase the risk of CAD. However, previous research has shown that low circulating IGF‐I concentrations are associated with an increased risk of developing IHD 23, 24, 25, 26. The reason for those discrepancies is not well known, the different diseases and racial/ethnic difference are a potential reason.
We show that CAD patients with a minimum of one mutant C allele, A/C or C/C, of the polymorphism of IGFBP‐3 −202 have less incidence of stroke, compared to A/A homozygote patients. Deal et al. showed that the C allele of IGFBP‐3 −202 expressed products with 50% lower activity, compared to the A allele in vitro 36. Moreover, the C allele of IGFBP‐3 −202 was involved in decreased IGFBP‐3 levels in serum (P < 0.01; 63), including in African Americans, Native Hawaiians, Japanese Americans, Latinos, and Caucasians 64. Mong et al. indicated that the C allele of IGFBP‐3 −202 contributed to reduced IGFBP‐3 levels (P = 1.21 × 10−13) and decreased LDL‐C (P = 0.020; 65). Li et al. showed that the A/A genotype of IGFBP‐3 −202 significantly reduced cancer risk in breast cancer and prostate cancer, compared to the CC genotype 66. Safarinejad showed that the A allele of IGFBP‐3 −202 significantly reduced cancer risk of clear cell renal cell carcinoma (OR = 4.75, 95%; CI = 3.64–7.64, P = 0.001; 67). Our study shows that the polymorphism of IGFBP‐3 −202 may have an association with decreased risk in stroke and CAD patients. Therefore, the C allele, A/C or C/C, of the polymorphism of IGFBP‐3 −202 may exert a protective function from stroke in CAD patients.
In conclusion, our results show that the polymorphism of IGF‐I +1770 is associated with increased CAD risk. Furthermore, in CAD patients, the polymorphisms of IGF‐I +1770 and +6093 may have an influence on blood pressure during the pathogenesis of CAD. The polymorphism of IGF‐I +6093 may have an association with increased CHF risk in CAD patients; however, the polymorphism of IGFBP‐3 −202 may have an association with decreased stroke risk in CAD patients. With further investigation of the mechanism in which IGF‐I and IGFBP‐3 is involved, IGF‐I and IGFBP‐3 might be potential candidates for the therapy and diagnosis of CAD.
REFERENCES
- 1. Delafontaine P, Song YH, Li Y. Expression, regulation, and function of IGF‐1, IGF‐1R, and IGF‐1 binding proteins in blood vessels. Arterioscler Thromb Vasc Biol 2004;24:435–444. [DOI] [PubMed] [Google Scholar]
- 2. Humbel RE. Insulin‐like growth factors I and II . Eur J Biochem 1990;190:445–462. [DOI] [PubMed] [Google Scholar]
- 3. Le Roith D, Bondy C, Yakar S, Liu JL, Butler A. The somatomedin hypothesis: 2001. Endocr Rev 2001;22:53–74. [DOI] [PubMed] [Google Scholar]
- 4. Samani AA, Yakar S, LeRoith D, Brodt P. The role of the IGF system in cancer growth and metastasis: Overview and recent insights. Endocr Rev 2007;28:20–47. [DOI] [PubMed] [Google Scholar]
- 5. Jones JI, Clemmons DR. Insulin‐like growth factors and their binding proteins: biological actions. Endocr Rev 1995;16:3–34. [DOI] [PubMed] [Google Scholar]
- 6. Clemmons DR. Role of insulin‐like growth factor binding proteins in controlling IGF actions. Mol Cell Endocrinol 1998;140:19–24. [DOI] [PubMed] [Google Scholar]
- 7. Burchardt P, Gozdzicka‐Jozefiak A, Siminiak T. IGF‐1—A new risk factor for coronary atherosclerosis. Kardiol Pol 2006;64:1297–1302. [PubMed] [Google Scholar]
- 8. Khandwala HM, McCutcheon IE, Flyvbjerg A, Friend KE. The effects of insulin‐like growth factors on tumorigenesis and neoplastic growth. Endocr Rev 2000;21:215–244. [DOI] [PubMed] [Google Scholar]
- 9. Clayton PE, Banerjee I, Murray PG, Renehan AG. Growth hormone, the insulin‐like growth factor axis, insulin and cancer risk. Nat Rev Endocrinol 2011;7:11–24. [DOI] [PubMed] [Google Scholar]
- 10. Renehan AG, Zwahlen M, Minder C, O'Dwyer ST, Shalet SM, Egger M. Insulin‐like growth factor (IGF)‐I, IGF binding protein‐3, and cancer risk: Systematic review and meta‐regression analysis. Lancet 2004;363:1346–1353. [DOI] [PubMed] [Google Scholar]
- 11. Bubley GJ, Balk SP, Regan MM, et al. Serum levels of insulin‐like growth factor‐1 and insulin‐like growth factor‐1 binding proteins after radical prostatectomy. Urol 2002;168:2249–2252. [DOI] [PubMed] [Google Scholar]
- 12. Moller DE, Flier JS. Insulin resistance—Mechanisms, syndromes, and implications. N Engl J Med 1991;325:938–948. [DOI] [PubMed] [Google Scholar]
- 13. Burchardt P, Gozdzicka‐Jozefiak A, Zurawski J, et al. Are elevated levels of IGF‐1 caused by coronary arteriesoclerosis? Molecular and clinical analysis. Protein J 2010;29:538–544 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14. Rowlands MA, Gunnell D, Harris R, Vatten LJ, Holly JM, Martin RM. Circulating insulin‐like growth factor peptides and prostate cancer risk: A systematic review and meta‐analysis. Int J Cancer 2009;124:2416–2429. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. Gennigens C, Menetrier‐Caux C, Droz JP. Insulin‐like growth factor (IGF) family and prostate cancer. Crit Rev Oncol Hematol 2006;58:124–145. [DOI] [PubMed] [Google Scholar]
- 16. Roddam AW, Allen NE, Appleby P, et al. Insulin‐like growth factors, their binding proteins, and prostate cancer risk: Analysis of individual patient data from 12 prospective studies. Ann Intern Med 2008;149:461–471. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17. Giovannucci E. Insulin and colon cancer. Cancer Causes Control 1995;6:164–179. [DOI] [PubMed] [Google Scholar]
- 18. Richard E. Weitzman Memorial Award of the Endocrine Society to Mitchell Avery Lazar. Endocr Rev 1995;16:557–558. [PubMed] [Google Scholar]
- 19. DeLellis K, Rinaldi S, Kaaks RJ, Kolonel LN, Henderson B, Le Marchand L. Dietary and lifestyle correlates of plasma insulin‐like growth factor‐I (IGF‐I) and IGF binding protein‐3 (IGFBP‐3): The multiethnic cohort. Cancer Epidemiol Biomarkers Prev 2004;13:1444–1451. [PubMed] [Google Scholar]
- 20. Rinaldi S, Cleveland R, Norat T, et al. Serum levels of IGF‐I, IGFBP‐3 and colorectal cancer risk: Results from the EPIC cohort, plus a meta‐analysis of prospective studies. Int J Cancer 2010;126:1702–1715. [DOI] [PubMed] [Google Scholar]
- 21. Fletcher O, Gibson L, Johnson N, et al. Polymorphisms and circulating levels in the insulin‐like growth factor system and risk of breast cancer: A systematic review. Cancer Epidemiol Biomarkers Prev 2005;14:2–19. [PubMed] [Google Scholar]
- 22. Markussis V, Beshyah SA, Fisher C. Detection of premature atherosclerosis by high‐resolution ultrasonography in symptom‐free hypopituitary adults. Lancet 1992;340:1188–1192. [DOI] [PubMed] [Google Scholar]
- 23. Spallarossa P, Brunelli C, Minuto F, et al. Insulin‐like growth factor‐I and angiographically documented coronary artery disease. Am J Cardiol 1996;77:200–202. [DOI] [PubMed] [Google Scholar]
- 24. Goodman‐Gruen D, Barrett‐Connor E, Rosen C. IGF‐1 and ischemic heart disease in older people. J Am Geriatr Soc 2000;48:860–861. [DOI] [PubMed] [Google Scholar]
- 25. Janssen JA, Stolk RP, Pols HA, Grobbee DE, Lamberts SW. Serum total IGF‐I, free IGF‐I, and IGFB‐1 levels in an elderly population: Relation to cardiovascular risk factors and disease. Arterioscler Thromb Vasc Biol 1998;18:277–282. [DOI] [PubMed] [Google Scholar]
- 26. Juul A, Scheike T, Davidsen M, Gyllenborg J, Jorgensen T. Low serum insulin‐like growth factor I is associated with increased risk of ischemic heart disease: A population‐based case‐control study. Circulation 2002;106:939–944. [DOI] [PubMed] [Google Scholar]
- 27. Abe N, Matsunaga T, Kameda K, et al. Increased level of pericardial insulin‐like growth factor‐1 in patients with left ventricular dysfunction and advanced heart failure. J Am Coll Cardiol 2006;48:1387–1395. [DOI] [PubMed] [Google Scholar]
- 28. Johnsen SP, Hundborg HH, Sorensen HT, et al. Insulin‐like growth factor (IGF) I, ‐II, and IGF binding protein‐3 and risk of ischemic stroke. J Clin Endocrinol Metab 2005;90:5937–5941. [DOI] [PubMed] [Google Scholar]
- 29. Janssen JA, Lamberts SW. The role of IGF‐I in the development of cardiovascular disease in type 2 diabetes mellitus: is prevention possible? Eur J Endocrinol 2002;146:467–477. [DOI] [PubMed] [Google Scholar]
- 30. van den Beld AW, Bots ML, Janssen JA, Pols HA, Lamberts SW, Grobbee DE. Endogenous hormones and carotid atherosclerosis in elderly men. Am J Epidemiol 2003;157:25–31. [DOI] [PubMed] [Google Scholar]
- 31. Kelley KM, Oh Y, Gargosky SE, et al. Insulin‐like growth factor‐binding proteins (IGFBPs) and their regulatory dynamics. Int J Biochem Cell Biol 1996;28:619–637. [DOI] [PubMed] [Google Scholar]
- 32. Boger RH, Frystyk J, Ledet T, Moller N, Flyvbjerg A, Orskov H. Low serum insulin‐like growth factor I is associated with increased risk of ischemic heart disease. Circulation 2003;107:e193. [DOI] [PubMed] [Google Scholar]
- 33. Schuler‐Luttmann S, Monnig G, Enbergs A, et al. Insulin‐like growth factor‐binding protein‐3 is associated with the presence and extent of coronary arteriosclerosis. Arterioscler Thromb Vasc Biol 2000;20:E10–E15. [PubMed] [Google Scholar]
- 34. Weng CJ, Hsieh YH, Tsai CM, et al. Relationship of insulin‐like growth factors system gene polymorphisms with the susceptibility and pathological development of hepatocellular carcinoma. Ann Surg Oncol 2010;17:1808–1815. [DOI] [PubMed] [Google Scholar]
- 35. Yoon AJ, Zavras AI, Chen MK, Lin CW, Yang SF. Association between Gly1619ARG polymorphism of IGF2R domain 11 (rs629849) and advanced stage of oral cancer. Med Oncol 2012;29:682–685. [DOI] [PubMed] [Google Scholar]
- 36. Cheng I, Stram DO, Penney KL, et al. Common genetic variation in IGF1 and prostate cancer risk in the multiethnic cohort. J Natl Cancer Inst 2006;98:123–134. [DOI] [PubMed] [Google Scholar]
- 37. Hoyo C, Grubber J, Demark‐Wahnefried W, et al. Grade‐specific prostate cancer associations of IGF1 (CA)19 repeats and IGFBP3–202A/C in blacks and whites. J Natl Med Assoc 2007;99:718–722. [PMC free article] [PubMed] [Google Scholar]
- 38. Deal C, Ma J, Wilkin F, et al. Novel promoter polymorphism in insulin‐like growth factor‐binding protein‐3: Correlation with serum levels and interaction with known regulators. J Clin Endocrinol Metab 2001;86:1274–1280. [DOI] [PubMed] [Google Scholar]
- 39. Han S, Peng J, Zhang R, et al. Lack of association of GH T1663A variation and IGFBP3–202A/C polymorphism with risk of ischemic stroke in Chinese. Clin Chem Lab Med 2008;46:1056–1058. [DOI] [PubMed] [Google Scholar]
- 40. WHO publishes definitive atlas on global heart disease and stroke epidemic . Indian J Med Sci 2004;58:405–406. [PubMed] [Google Scholar]
- 41. Conti E, Carrozza C, Capoluongo E, et al. Insulin‐like growth factor‐1 as a vascular protective factor. Circulation 2004;110:2260–2205. [DOI] [PubMed] [Google Scholar]
- 42. Heald AH, Golding C, Sharma R, et al. A substitution model of dietary manipulation is an effective means of optimising lipid profile, reducing C‐reactive protein and increasing insulin‐like growth factor‐1. Br J Nutr 2004;92:809–818. [DOI] [PubMed] [Google Scholar]
- 43. Conti E, Musumeci MB, De Giusti M, et al. IGF‐1 and atherothrombosis: Relevance to pathophysiology and therapy. Clin Sci (Lond) 2011;120:377–402. [DOI] [PubMed] [Google Scholar]
- 44. Lam CS, Chen MH, Lacey SM, et al. Circulating insulin‐like growth factor‐1 and its binding protein‐3: Metabolic and genetic correlates in the community. Arterioscler Thromb Vasc Biol 2010;30:1479–1484. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45. Shai SY, Sukhanov S, Higashi Y, Vaughn C, Kelly J, Delafontaine P. Smooth muscle cell‐specific insulin‐like growth factor‐1 overexpression in Apoe‐/‐ mice does not alter atherosclerotic plaque burden but increases features of plaque stability. Arterioscler Thromb Vasc Biol 2010;30:1916–1924. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46. Fernandez‐Hernando C, Ackah E, Yu J, et al. Loss of Akt1 leads to severe atherosclerosis and occlusive coronary artery disease. Cell Metab 2007;6:446–457. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47. Fernandez‐Hernando C, Jozsef L, Jenkins D, Di Lorenzo A, Sessa WC. Absence of Akt1 reduces vascular smooth muscle cell migration and survival and induces features of plaque vulnerability and cardiac dysfunction during atherosclerosis. Arterioscler Thromb Vasc Biol 2009;29:2033–2040. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48. Wickman A, Jonsdottir IH, Bergstrom G, Hedin L. GH and IGF‐I regulate the expression of endothelial nitric oxide synthase (eNOS) in cardiovascular tissues of hypophysectomized female rats. Eur J Endocrinol 2002;147:523–533. [DOI] [PubMed] [Google Scholar]
- 49. Schini‐Kerth VB. Dual effects of insulin‐like growth factor‐I on the constitutive and inducible nitric oxide (NO) synthase‐dependent formation of NO in vascular cells. J Endocrinol Invest 1999;22:82–88. [PubMed] [Google Scholar]
- 50. Schmidt‐Lucke C, Rossig L, Fichtlscherer S, et al. Reduced number of circulating endothelial progenitor cells predicts future cardiovascular events: Proof of concept for the clinical importance of endogenous vascular repair. Circulation 2005;111:2981–2987. [DOI] [PubMed] [Google Scholar]
- 51. Taylor WR, Alexander RW. Autocrine control of wound repair by insulin‐like growth factor I in cultured endothelial cells. Am J Physiol 1993;265:C801–C805. [DOI] [PubMed] [Google Scholar]
- 52. Harrela M, Koistinen H, Kaprio J, et al. Genetic and environmental components of interindividual variation in circulating levels of IGF‐I, IGF‐II, IGFBP‐1, and IGFBP‐3. J Clin Invest 1996;98:2612–2615. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53. Kao PC, Matheny AP, Jr. , Lang CA. Insulin‐like growth factor‐I comparisons in healthy twin children. J Clin Endocrinol Metab 1994;78:310–312. [DOI] [PubMed] [Google Scholar]
- 54. Juul A, Main K, Blum WF, Lindholm J, Ranke MB, Skakkebaek NE. The ratio between serum levels of insulin‐like growth factor (IGF)‐I and the IGF binding proteins (IGFBP‐1, 2 and 3) decreases with age in healthy adults and is increased in acromegalic patients. Clin Endocrinol 1994;41:85–93. [DOI] [PubMed] [Google Scholar]
- 55. Norat T, Dossus L, Rinaldi S, et al. Diet, serum insulin‐like growth factor‐I and IGF‐binding protein‐3 in European women. Eur J Clin Nutr 2007;61:91–98. [DOI] [PubMed] [Google Scholar]
- 56. Holmes MD, Pollak MN, Willett WC, Hankinson SE. Dietary correlates of plasma insulin‐like growth factor I and insulin‐like growth factor binding protein 3 concentrations. Cancer Epidemiol Biomarkers Prev 2002;11:852–861. [PubMed] [Google Scholar]
- 57. Lukanova A, Toniolo P, Akhmedkhanov A, et al. A cross‐sectional study of IGF‐I determinants in women. Eur J Cancer Prev 2001;10:443–452. [DOI] [PubMed] [Google Scholar]
- 58. Vaessen N, Heutink P, Janssen JA, et al. A polymorphism in the gene for IGF‐I: Functional properties and risk for type 2 diabetes and myocardial infarction. Diabetes 2001;50:637–642. [DOI] [PubMed] [Google Scholar]
- 59. Yazdanpanah M, Rietveld I, Janssen JA, et al. An insulin‐like growth factor‐I promoter polymorphism is associated with increased mortality in subjects with myocardial infarction in an elderly Caucasian population. Am J Cardiol 2006;97:1274–1276. [DOI] [PubMed] [Google Scholar]
- 60. Rietveld I, Janssen JA, Hofman A, Pols HA, van Duijn CM, Lamberts SW. A polymorphism in the IGF‐I gene influences the age‐related decline in circulating total IGF‐I levels. Eur J Endocrinol 2003;148:171–175. [DOI] [PubMed] [Google Scholar]
- 61. Sarma AV, Dunn RL, Lange LA, et al. Genetic polymorphisms in CYP17, CYP3A4, CYP19A1, SRD5A2, IGF‐1, and IGFBP‐3 and prostate cancer risk in African‐American men: The Flint Men's health study. Prostate 2008;68:296–305. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 62. Al‐Zahrani A, Sandhu MS, Luben RN, et al. IGF1 and IGFBP3 tagging polymorphisms are associated with circulating levels of IGF1, IGFBP3 and risk of breast cancer. Hum Mol Genet 2006;15:1–10. [DOI] [PubMed] [Google Scholar]
- 63. Su X, Colditz GA, Willett WC, et al. Genetic variation and circulating levels of IGF‐I and IGFBP‐3 in relation to risk of proliferative benign breast disease. Int J Cancer 2010;126:180–190. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 64. Hicks AA, Pramstaller PP, Johansson A, et al. Genetic determinants of circulating sphingolipid concentrations in European populations. PLoS Genet 2009;5:e1000672. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65. Mong JL, Ng MC, Guldan GS, et al. Associations of insulin‐like growth factor binding protein‐3 gene polymorphisms with IGF‐I activity and lipid parameters in adolescents. Int J Obes (Lond) 2009;33:1446–53. [DOI] [PubMed] [Google Scholar]
- 66. Li L, Huang X, Huo K. IGFBP3 polymorphisms and risk of cancer: A meta‐analysis. Mol Biol Rep 2010;37:127–140. [DOI] [PubMed] [Google Scholar]
- 67. Safarinejad MR. Insulin‐like growth factor binding protein‐3 (IGFBP‐3) gene variants are associated with renal cell carcinoma. BJU Int 2011;108:762–770. [DOI] [PubMed] [Google Scholar]